NEURAL NETWORK BASED VOLTAGE STABILITY IMPROVEMENT OF THE NIGERIAN 330KV POWER NETWORK USING UNIFIED POWER FLOW CONTROLLER (UPFC) AND HIGH VOLTAGE DIRECT CURRENT (HVDC) DURING CONTINGENCY OF LARGE LOAD GAIN.
Abstract
This paper presents the neural network-based voltage stability improvement of the Nigeria 330KV Power transmission network using a Unified Power Flow Controller (UPFC) and High Voltage Direct Current (HVDC). The impact of voltage instabilities in our power networks can be damaging. Load changes, loss of generators, amongst other contingencies, can introduce voltage instability in the networks. In this research, a parallel operation of the Unified Power Flow Controller (UPFC) and High Voltage Direct Current (HVDC) is employed to enhance the voltage stability of the Nigerian network under a contingency of large load gain. HVDC and UPFC have become very attractive in network compensation due to their ability to independently control real and reactive power in a network. Simulink models of the test network, the UPFC and HVDC, were developed in Matlab for simulation. Neural network controllers designed and implemented in Simulink Matlab effectively controlled the devices to achieve the desired result when the network was simulated. Results obtained from simulations revealed that relative to when the devices were not connected to the network, a parallel operation of HVDC and UPFC improved the voltage stability of the network by 43.8%. It can be concluded that the operation of HVDC and UPFC together in a power transmission network is effective in enhancing voltage stability of the network during a contingency of large gain in load.
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Traffic light control,, Fuzzy logic, Mamdani method,, Traffic management,, Vehicle density detectionDownloads
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